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Genetic Algorithm Based Decentralized Task Assignment for Multiple Unmanned Aerial Vehicles in Dynamic Environments
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 Title & Authors
Genetic Algorithm Based Decentralized Task Assignment for Multiple Unmanned Aerial Vehicles in Dynamic Environments
Choi, Hyun-Jin; Kim, You-Dan; Kim, Hyoun-Jin;
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 Abstract
Task assignments of multiple unmanned aerial vehicles (UAVs) are examined. The phrase "task assignment" comprises the decision making procedures of a UAV group. In this study, an on-line decentralized task assignment algorithm is proposed for an autonomous UAV group. The proposed method is divided into two stages: an order optimization stage and a communications and negotiation stage. A genetic algorithm and negotiation strategy based on one-to-one communication is adopted for each stage. Through the proposed algorithm, decentralized task assignments can be applied to dynamic environments in which sensing range and communication are limited. The performance of the proposed algorithm is verified by performing numerical simulations.
 Keywords
Decentralized Task Assignment;Multiple unmanned aerial vehicles;Combinatorial Optimization;Genetic Algorithm;Negotiation;
 Language
English
 Cited by
1.
Coordinated road-network search route planning by a team of UAVs, International Journal of Systems Science, 2014, 45, 5, 825  crossref(new windwow)
2.
Coordinated Standoff Tracking Using Path Shaping for Multiple UAVs, IEEE Transactions on Aerospace and Electronic Systems, 2014, 50, 1, 348  crossref(new windwow)
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